Just last week, two studies individually unveiled computer chips modeled after information processing in the human brain.

The first, published in Nature Materials, found a perfect solution to deal with unpredictability at synapses—the gap between two neurons that transmit and store information. The second, published in Science Advances, further amped up the system’s computational power, filling synapses with nanoclusters of supermagnetic material to bolster information encoding.

The result? Brain-like hardware systems that compute faster—and more efficiently—than the human brain.

“Ultimately we want a chip as big as a fingernail to replace one big supercomputer,” said Dr. Jeehwan Kim, who led the first study at MIT in Cambridge, Massachusetts.

Experts are hopeful.

“The field’s full of hype, and it’s nice to see quality work presented in an objective way,” said Dr. Carver Mead, an engineer at the California Institute of Technology in Pasadena not involved in the work.

Software to Hardware
The human brain is the ultimate computational wizard. With roughly 100 billion neurons densely packed into the size of a small football, the brain can deftly handle complex computation at lightning speed using very little energy.

AI experts have taken note. The past few years saw brain-inspired algorithms that can identify faces, falsify voices, and play a variety of games at—and often above—human capability.

But software is only part of the equation. Our current computers, with their transistors and binary digital systems, aren’t equipped to run these powerful algorithms.

That’s where neuromorphic computing comes in. The idea is simple: fabricate a computer chip that mimics the brain at the hardware level. Here, data is both processed and stored within the chip in an analog manner. Each artificial synapse can accumulate and integrate small bits of information from multiple sources and fire only when it reaches a threshold—much like its biological counterpart.

Experts believe the speed and efficiency gains will be enormous.

For one, the chips will no longer have to transfer data between the central processing unit (CPU) and storage blocks, which wastes both time and energy. For another, like biological neural networks, neuromorphic devices can support neurons that run millions of streams of parallel computation.

A “Brain-on-a-chip”
Optimism aside, reproducing the biological synapse in hardware form hasn’t been as easy as anticipated.

Neuromorphic chips exist in many forms, but often look like a nanoscale metal sandwich. The “bread” pieces are generally made of conductive plates surrounding a switching medium—a conductive material of sorts that acts like the gap in a biological synapse.

When a voltage is applied, as in the case of data input, ions move within the switching medium, which then creates conductive streams to stimulate the downstream plate. This change in conductivity mimics the way biological neurons change their “weight,” or the strength of connectivity between two adjacent neurons.

But so far, neuromorphic synapses have been rather unpredictable. According to Kim, that’s because the switching medium is often comprised of material that can’t channel ions to exact locations on the downstream plate.

“Once you apply some voltage to represent some data with your artificial neuron, you have to erase and be able to write it again in the exact same way,” explains Kim. “But in an amorphous solid, when you write again, the ions go in different directions because there are lots of defects.”

In his new study, Kim and colleagues swapped the jelly-like switching medium for silicon, a material with only a single line of defects that acts like a channel to guide ions.

The chip starts with a thin wafer of silicon etched with a honeycomb-like pattern. On top is a layer of silicon germanium—something often present in transistors—in the same pattern. This creates a funnel-like dislocation, a kind of Grand Canal that perfectly shuttles ions across the artificial synapse.

The researchers then made a neuromorphic chip containing these synapses and shot an electrical zap through them. Incredibly, the synapses’ response varied by only four percent—much higher than any neuromorphic device made with an amorphous switching medium.

In a computer simulation, the team built a multi-layer artificial neural network using parameters measured from their device. After tens of thousands of training examples, their neural network correctly recognized samples 95 percent of the time, just 2 percent lower than state-of-the-art software algorithms.

The upside? The neuromorphic chip requires much less space than the hardware that runs deep learning algorithms. Forget supercomputers—these chips could one day run complex computations right on our handheld devices.

A Magnetic Boost
Meanwhile, in Boulder, Colorado, Dr. Michael Schneider at the National Institute of Standards and Technology also realized that the standard switching medium had to go.

“There must be a better way to do this, because nature has figured out a better way to do this,” he says.

His solution? Nanoclusters of magnetic manganese.

Schneider’s chip contained two slices of superconducting electrodes made out of niobium, which channel electricity with no resistance. When researchers applied different magnetic fields to the synapse, they could control the alignment of the manganese “filling.”

The switch gave the chip a double boost. For one, by aligning the switching medium, the team could predict the ion flow and boost uniformity. For another, the magnetic manganese itself adds computational power. The chip can now encode data in both the level of electrical input and the direction of the magnetisms without bulking up the synapse.

It seriously worked. At one billion times per second, the chips fired several orders of magnitude faster than human neurons. Plus, the chips required just one ten-thousandth of the energy used by their biological counterparts, all the while synthesizing input from nine different sources in an analog manner.

The Road Ahead
These studies show that we may be nearing a benchmark where artificial synapses match—or even outperform—their human inspiration.

But to Dr. Steven Furber, an expert in neuromorphic computing, we still have a ways before the chips go mainstream.

Many of the special materials used in these chips require specific temperatures, he says. Magnetic manganese chips, for example, require temperatures around absolute zero to operate, meaning they come with the need for giant cooling tanks filled with liquid helium—obviously not practical for everyday use.

Another is scalability. Millions of synapses are necessary before a neuromorphic device can be used to tackle everyday problems such as facial recognition. So far, no deal.

But these problems may in fact be a driving force for the entire field. Intense competition could push teams into exploring different ideas and solutions to similar problems, much like these two studies.

If so, future chips may come in diverse flavors. Similar to our vast array of deep learning algorithms and operating systems, the computer chips of the future may also vary depending on specific requirements and needs.

Robotics research has been making great strides in recent years, but there are still many hurdles to the machines becoming a ubiquitous presence in our lives. The journal Science Robotics has now identified 10 grand challenges the field will have to grapple with to make that a reality.

Editors conducted an online survey on unsolved challenges in robotics and assembled an expert panel of roboticists to shortlist the 30 most important topics, which were then grouped into 10 grand challenges that could have major impact in the next 5 to 10 years. Here’s what they came up with.

1. New Materials and Fabrication Schemes
Roboticists are beginning to move beyond motors, gears, and sensors by experimenting with things like artificial muscles, soft robotics, and new fabrication methods that combine multiple functions in one material. But most of these advances have been “one-off” demonstrations, which are not easy to combine.

Multi-functional materials merging things like sensing, movement, energy harvesting, or energy storage could allow more efficient robot designs. But combining these various properties in a single machine will require new approaches that blend micro-scale and large-scale fabrication techniques. Another promising direction is materials that can change over time to adapt or heal, but this requires much more research.

2. Bioinspired and Bio-Hybrid Robots
Nature has already solved many of the problems roboticists are trying to tackle, so many are turning to biology for inspiration or even incorporating living systems into their robots. But there are still major bottlenecks in reproducing the mechanical performance of muscle and the ability of biological systems to power themselves.

There has been great progress in artificial muscles, but their robustness, efficiency, and energy and power density need to be improved. Embedding living cells into robots can overcome challenges of powering small robots, as well as exploit biological features like self-healing and embedded sensing, though how to integrate these components is still a major challenge. And while a growing “robo-zoo” is helping tease out nature’s secrets, more work needs to be done on how animals transition between capabilities like flying and swimming to build multimodal platforms.

3. Power and Energy
Energy storage is a major bottleneck for mobile robotics. Rising demand from drones, electric vehicles, and renewable energy is driving progress in battery technology, but the fundamental challenges have remained largely unchanged for years.

That means that in parallel to battery development, there need to be efforts to minimize robots’ power utilization and give them access to new sources of energy. Enabling them to harvest energy from their environment and transmitting power to them wirelessly are two promising approaches worthy of investigation.

4. Robot Swarms
Swarms of simple robots that assemble into different configurations to tackle various tasks can be a cheaper, more flexible alternative to large, task-specific robots. Smaller, cheaper, more powerful hardware that lets simple robots sense their environment and communicate is combining with AI that can model the kind of behavior seen in nature’s flocks.

But there needs to be more work on the most efficient forms of control at different scales—small swarms can be controlled centrally, but larger ones need to be more decentralized. They also need to be made robust and adaptable to the changing conditions of the real world and resilient to deliberate or accidental damage. There also needs to be more work on swarms of non-homogeneous robots with complementary capabilities.

5. Navigation and Exploration
A key use case for robots is exploring places where humans cannot go, such as the deep sea, space, or disaster zones. That means they need to become adept at exploring and navigating unmapped, often highly disordered and hostile environments.

The major challenges include creating systems that can adapt, learn, and recover from navigation failures and are able to make and recognize new discoveries. This will require high levels of autonomy that allow the robots to monitor and reconfigure themselves while being able to build a picture of the world from multiple data sources of varying reliability and accuracy.

6. AI for Robotics
Deep learning has revolutionized machines’ ability to recognize patterns, but that needs to be combined with model-based reasoning to create adaptable robots that can learn on the fly.

Key to this will be creating AI that’s aware of its own limitations and can learn how to learn new things. It will also be important to create systems that are able to learn quickly from limited data rather than the millions of examples used in deep learning. Further advances in our understanding of human intelligence will be essential to solving these problems.

7. Brain-Computer Interfaces
BCIs will enable seamless control of advanced robotic prosthetics but could also prove a faster, more natural way to communicate instructions to robots or simply help them understand human mental states.

Most current approaches to measuring brain activity are expensive and cumbersome, though, so work on compact, low-power, and wireless devices will be important. They also tend to involve extended training, calibration, and adaptation due to the imprecise nature of reading brain activity. And it remains to be seen if they will outperform simpler techniques like eye tracking or reading muscle signals.

8. Social Interaction
If robots are to enter human environments, they will need to learn to deal with humans. But this will be difficult, as we have very few concrete models of human behavior and we are prone to underestimate the complexity of what comes naturally to us.

Social robots will need to be able to perceive minute social cues like facial expression or intonation, understand the cultural and social context they are operating in, and model the mental states of people they interact with to tailor their dealings with them, both in the short term and as they develop long-standing relationships with them.

9. Medical Robotics
Medicine is one of the areas where robots could have significant impact in the near future. Devices that augment a surgeon’s capabilities are already in regular use, but the challenge will be to increase the autonomy of these systems in such a high-stakes environment.

Autonomous robot assistants will need to be able to recognize human anatomy in a variety of contexts and be able to use situational awareness and spoken commands to understand what’s required of them. In surgery, autonomous robots could perform the routine steps of a procedure, giving way to the surgeon for more complicated patient-specific bits.

Micro-robots that operate inside the human body also hold promise, but there are still many roadblocks to their adoption, including effective delivery systems, tracking and control methods, and crucially, finding therapies where they improve on current approaches.

10. Robot Ethics and Security
As the preceding challenges are overcome and robots are increasingly integrated into our lives, this progress will create new ethical conundrums. Most importantly, we may become over-reliant on robots.

That could lead to humans losing certain skills and capabilities, making us unable to take the reins in the case of failures. We may end up delegating tasks that should, for ethical reasons, have some human supervision, and allow people to pass the buck to autonomous systems in the case of failure. It could also reduce self-determination, as human behaviors change to accommodate the routines and restrictions required for robots and AI to work effectively.

Global efforts to expand the capabilities of industrial robots are on the rise, as the demand from manufacturing companies to strengthen their operations and improve performance grows.

Hungary-based OptoForce, with a North American office in Charlotte, North Carolina, is one company that continues to support organizations with new robotic capabilities, as evidenced by its several new applications released in 2017.

The company, a leading robotics technology provider of multi-axis force and torque sensors, delivers 6 degrees of freedom force and torque measurement for industrial automation, and provides sensors for most of the currently-used industrial robots.

It recently developed and brought to market three new applications for KUKA industrial robots.

The new applications are hand guiding, presence detection, and center pointing and will be utilized by both end users and systems integrators. Each application is summarized below and what they provide for KUKA robots, along with video demonstrations to show how they operate.

Photo By: www.optoforce.com

Hand Guiding: With OptoForce’s Hand Guiding application, KUKA robots can easily and smoothly move in an assigned direction and selected route. This video shows specifically how to program the robot for hand guiding.

Presence Detection: This application allows KUKA robots to detect the presence of a specific object and to find the object even if it has moved. Visit here to learn more about presence detection.
Center Pointing: With this application, the OptoForce sensor helps the KUKA robot find the center point of an object by providing the robot with a sense of touch. This solution also works with glossy metal objects where a vision system would not be able to define its position. This video shows in detail how the center pointing application works.

The company’s CEO explained how these applications help KUKA robots and industrial automation.

Photo By: www.optoforce.com
“OptoForce’s new applications for KUKA robots pave the way for substantial improvements in industrial automation for both end users and systems integrators,” said Ákos Dömötör, CEO of OptoForce. “Our 6-axis force/torque sensors are combined with highly functional hardware and a comprehensive software package, which include the pre-programmed industrial applications. Essentially, we’re adding a ‘sense of touch’ to KUKA robot arms, enabling these robots to have abilities similar to a human hand, and opening up numerous new capabilities in industrial automation.”

Along with these new applications recently released for KUKA robots, OptoForce sensors are also being used by various companies on numerous industrial robots and manufacturing automation projects around the world. Examples of other uses include: path recording, polishing plastic and metal, box insertion, placing pins in holes, stacking/destacking, palletizing, and metal part sanding.

Specifically, some of the projects current underway by companies include: a plastic parting line removal; an obstacle detection for a major car manufacturing company; and a center point insertion application for a car part supplier, where the task of the robot is to insert a mirror, completely centered, onto a side mirror housing.

For more information, visit www.optoforce.com.

This post was provided by: OptoForce

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